Force estimation from 4D OCT data in a human tumor xenograft mouse model

Abstract

Minimally invasive robotic surgery offer benefits such as reduced physical trauma, faster recovery and lesser pain for the patient. For these procedures, visual and haptic feedback to the surgeon is crucial when operating surgical tools without line-of-sight with a robot. External force sensors are biased by friction at the tool shaft and thereby cannot estimate forces between tool tip and tissue. As an alternative, vision-based force estimation was proposed. Here, interaction forces are directly learned from deformation observed by an external imaging system. Recently, an approach based on optical coherence tomography and deep learning has shown promising results. However, most experiments are performed on ex-vivo tissue. In this work, we demonstrate that models trained on dead tissue do not perform well in in vivo data. We performed multiple experiments on a human tumor xenograft mouse model, both on in vivo, perfused tissue and dead tissue. We compared two deep learning models in different training scenarios. Training on perfused, in vivo data improved model performance by 24% for in vivo force estimation.

Optical force estimation for interactions between tool and soft tissues

“…OCT has been considered for accurate image-based force estimation with single volumes 28 and 4D temporal sequences as input 29 . Promising results with OCT based force estimation were also demonstrated on xenograft mouse models with vascularized prostate tumors 30 . Additionally, OCE is ideal for local elasticity estimates due to the small field of view (FOV) and its high spatial and temporal resolution.…”